class_docstr: Character-level CNN encoder to generate word representations, as input to
  transformer encoder.
docstr: Maximum input length supported by the encoder.
docstr_tok: ["Maximum","input","length","supported","by","the","encoder","."]
code: def max_positions(self):
      """Maximum input length supported by the encoder."""
      return self.embed_positions.max_positions
  
imports: import logging
  import math
  from ast import literal_eval
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq.models import FairseqEncoder, register_model, register_model_architecture, transformer
  from fairseq.modules import SinusoidalPositionalEmbedding
  from pytorch_translate import char_encoder, transformer, utils, vocab_constants
  from pytorch_translate.char_source_model import verify_pretrain_params
  from pytorch_translate.common_layers import TransformerEncoderGivenEmbeddings, VariableTracker
  from pytorch_translate.data.dictionary import TAGS
label_desc: Use image node_id 7 for calling the CharCNNEncoder obj's underlying member method code with example usage: obj.max_positions() and returns: self